The massive MIMO communication technique, which employs tens or even hundreds of antenna elements (AEs) at a base station (BS) for communicating with a user equipment (UE), is to be adopted in forthcoming mobile communication systems. The advantage of using a large number of AEs is to provide a large beamforming gain, a high spatial resolution, a large diversity gain and a large spatial-multiplexing gain. In order to optimize data transmission for approaching the channel capacity of a MIMO communication system, the BS transmits channel state information- (CSI-) reference signal (RS) to the UE for estimating the MIMO channel, and the UE returns CSI to the BS so as to enable the BS to optimize the data transmission.
In implementing a BS with a large number of AEs, the conventional fully digital beamforming methods which require one transceiver unit (TXRU) per antenna element is not cost effective. Therefore, the number of TXRUs is considered to be less than the number of AEs in practical massive MIMO systems. FIG. 1 depicts, according to the Long Term Evolution (LTE) and LTE-Advanced (LTE-A) specifications, a typical architecture of a BS 110 (known as an eNodeB in the LTE and LTE-A specifications) for optimizing data transmission under a constraint that the number of TXRUs 124 is less than the number of AEs 111. First, the number of antenna ports (APs) 113 is determined. According to the LTE or LTE-A specification, an AP is a logical input port to digital data symbols, defined such that the channel over which a symbol on the AP is conveyed can be inferred from the channel over which another symbol on the same AP is conveyed. The number of APs 113 is less than or equal to the number of TXRUs 124 and hence is less than or equal to the number of AEs 111.
The mapping of the APs 113 to the AEs 111 is done by an AP virtualization process 120 as follows. First, a digital modulation-symbol vector for input to the APs 113 are processed by an AP-to-TXRU virtualization operation 122, modeled by an AP-to-TXRU virtualization matrix VP, to yield a resultant modulation-symbol vector. The resultant modulation-symbol vector is converted to a plurality of analog TXRU signals by the TXRUs 124. The analog TXRU signals are then processed with a TXRU-to-AE virtualization operation 125, modeled by a TXRU-to-AE virtualization matrix VT, to give analog signals to be transmitted on the AEs 111. Analog beamforming is the main purpose of the TXRU-to-AE virtualization operation 125 so that an analog network comprising phase shifters and adders is usually used to implement the TXRU-to-AE virtualization operation 125. The analog signals are transmitted to a UE 180 having plural AEs 181 over a MIMO channel 150. The MIMO channel 150 has a channel transfer function H.
In data transmission, payload data 105 are mapped to one or more layers by layer mapping. The payload data 105 after layer mapping are precoded by a precoding operation 140, modeled by a precoding matrix W, for achieving spatial multiplexing or transmit diversity. Then the precoded data are processed by the AP virtualization process 120 for transmission over the AEs 111.
The BS 110 requires the UE 180 to estimate the MIMO channel 150 to optimize data transmission. The BS 110 generates a CSI-RS as a pilot signal for channel estimation. According to the LTE or LTE-A specification, a plurality of CSI-RS symbols 106 is first determined and is processed by the AP virtualization process 120 to generate the pilot signal. Upon receiving the pilot signal, the UE 180 recovers the CSI-RS symbols, and hence estimates {tilde over (H)}=HVTVP. Based on the estimated {tilde over (H)}, the UE 180 computes CSI and feedbacks the CSI to the BS 110 through an uplink channel 186. The CSI includes a channel quality indicator (CQI), a rank indication (RI), and a precoding matrix indicator (PMI). Regarding error performance, the combination of modulation scheme and transport block size as indicated in the returned CQI could be achieved with a transport block error probability not exceeding 0.1. For spatial multiplexing, the RI corresponds to the number of useful transmission layers. The UE reports which W is preferred to be used through the PMI.
There are some drawbacks with the current CSI acquisition scheme. First, the AP virtualization pattern is restricted to a limited number of choices. In the LTE and LTE-A specifications, there are only two choices, either a one-to-one mapping which corresponds to the so called “non-precoded CSI-RS” or a one-to-all (for each polarization) mapping which corresponds to the so called “beamformed CSI-RS”. Having a limited number of choices is not flexible for fully optimizing data transmission. Second, the UE 180 is required to determine the CQI, RI and PMI. Huge computation complexity is involved as the number of APs 113 is large for a massive MIMO system. Third, determining W under the given AP-to-TXRU virtualization matrix VP does not give optimal performance in data transmission for some AP-to-TXRU virtualization patterns. Suboptimal performance leads to an inefficient use of the MIMO channel 150.
In the art, there have been some efforts aimed at addressing the aforementioned problems. For approaching an optimal performance, US20160072562 suggests that the BS determines a finite set of basis vectors based on uplink measurement, and signals the UE with a selected basis-vector subset. The UE then calculates the coefficients with respect to the selected basis-vector subset based on the channel estimate and informs the BS. However, signaling the UE with the selected basis-vector subset induces extra signaling overhead. Furthermore, the UE is required to derive channel coefficients based on the selected subset, increasing the computation burden of the UE. In WO2016052824, new, extra CSI-RS type indicators with virtualization information are sent to the UE for the UE to determine the CQI, RI and PMI. Although the data transmission performance can be improved, this approach also suffers from extra signaling load and increased computation burden on the UE.
There is still a need in the art for a technique to improve data transmission performance by optimizing the use of the MIMO channel without placing extra burdens on signaling and on computation requirement of the UE.